Having effective diagnostic tools to identify problems with models is crucial for trustworthy AI.
The most common way to evaluate a model is with a test set.
But good testing performance does not imply that a system will generalize safely in deployment.
Assessing a model on a test set does not typically reveal failures such as spurious features, out of distribution inputs, and adversarial inputs.
As AI continues to be developed and deployed in safety-critical settings, systems failing to learn solutions that align with our goals for them could pose large risks [1]}, [2]}, [3]}, [4]}, [5]}, [6]}.
Thus, it is important to have scalable and effective tools for identifying problems with deep neural networks (DNNs).
